DCB takes cue from the LUCB algorithm for best-arm identification in bandits [1]}. At each step \(t\) , it identifies two parties, \(a^{t}\)  and \(b^{t}\) , that appear the most promising to win the overall election: these parties are picked based on their current number of wins and “leads” in individual constituencies. Subsequently the algorithm chooses a constituency each for \(a^{t}\)  and \(b^{t}\) , samples from which could “most” help distinguish the tally of the two. We provide a detailed specification of DCB in Appendix .
